A neural network dynamic model for temperature and relative humidity control under greenhouse

This paper deals with the development of dynamic models for the estimations of internal temperature and relative humidity of a greenhouse. Multilayers perceptron with 12 hidden neurons with a hyperbolic tangent as an activation function and which has been trained with Levenberg Marquardt (LM) algori...

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Bibliographic Details
Published in2015 Third International Workshop on RFID And Adaptive Wireless Sensor Networks (RAWSN) pp. 6 - 11
Main Authors Outanoute, M., Lachhab, A., Ed-dahhak, A., Selmani, A., Guerbaoui, M., Bouchikhi, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Computational modeling
Data models
Green products
greenhouse climate
Humidity
Mathematical model
measurement and control system
modeling
neural networks
prediction
Temperature measurement
Training
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Summary:This paper deals with the development of dynamic models for the estimations of internal temperature and relative humidity of a greenhouse. Multilayers perceptron with 12 hidden neurons with a hyperbolic tangent as an activation function and which has been trained with Levenberg Marquardt (LM) algorithm. The data used to compute the simulation model were acquired in an experimental greenhouse using a sampling time interval of 10 seconds. The greenhouse is automated with several sensors and actuators that were connected to an acquisition and control system based on a personal computer. A comparison of measured and simulated data for both temperature and relative humidity under greenhouse showed that the elaborated models were able to identify and forecast inside greenhouse conditions reasonably well.
ISBN:9781467380959
1467380954
DOI:10.1109/RAWSN.2015.7173270